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llvm-mca is a performance analysis tool that uses information
available in LLVM (e.g. scheduling models) to statically measure the performance
of machine code in a specific CPU.

Performance is measured in terms of throughput as well as processor resource
consumption. The tool currently works for processors with an out-of-order
backend, for which there is a scheduling model available in LLVM.

The main goal of this tool is not just to predict the performance of the code
when run on the target, but also help with diagnosing potential performance
issues.

Given an assembly code sequence, llvm-mca estimates the Instructions Per Cycle
(IPC), as well as hardware resource pressure. The analysis and reporting style
were inspired by the IACA tool from Intel.

llvm-mca allows the usage of special code comments to mark regions of
the assembly code to be analyzed. A comment starting with substring
LLVM-MCA-BEGIN marks the beginning of a code region. A comment starting with
substring LLVM-MCA-END marks the end of a code region. For example:

# LLVM-MCA-BEGIN My Code Region
...
# LLVM-MCA-END

Multiple regions can be specified provided that they do not overlap. A code
region can have an optional description. If no user-defined region is specified,
then llvm-mca assumes a default region which contains every
instruction in the input file. Every region is analyzed in isolation, and the
final performance report is the union of all the reports generated for every
code region.

Inline assembly directives may be used from source code to annotate the
assembly text:

If input is “-” or omitted, llvm-mca reads from standard
input. Otherwise, it will read from the specified filename.

If the -o option is omitted, then llvm-mca will send its output
to standard output if the input is from standard input. If the -o
option specifies “-“, then the output will also be sent to standard output.

Specify the output assembly variant for the report generated by the tool.
On x86, possible values are [0, 1]. A value of 0 (vic. 1) for this flag enables
the AT&T (vic. Intel) assembly format for the code printed out by the tool in
the analysis report.

Specify the size of the register file. When specified, this flag limits how
many physical registers are available for register renaming purposes. A value
of zero for this flag means “unlimited number of physical registers”.

Specify the size of the load queue in the load/store unit emulated by the tool.
By default, the tool assumes an unbound number of entries in the load queue.
A value of zero for this flag is ignored, and the default load queue size is
used instead.

Specify the size of the store queue in the load/store unit emulated by the
tool. By default, the tool assumes an unbound number of entries in the store
queue. A value of zero for this flag is ignored, and the default store queue
size is used instead.

Print all hardware statistics. This enables extra statistics related to the
dispatch logic, the hardware schedulers, the register file(s), and the retire
control unit. This option is disabled by default.

Prints resource pressure information based on the static information
available from the processor model. This differs from the resource pressure
view because it doesn’t require that the code is simulated. It instead prints
the theoretical uniform distribution of resource pressure for every
instruction in sequence.

llvm-mca takes assembly code as input. The assembly code is parsed
into a sequence of MCInst with the help of the existing LLVM target assembly
parsers. The parsed sequence of MCInst is then analyzed by a Pipeline module
to generate a performance report.

The Pipeline module simulates the execution of the machine code sequence in a
loop of iterations (default is 100). During this process, the pipeline collects
a number of execution related statistics. At the end of this process, the
pipeline generates and prints a report from the collected statistics.

Here is an example of a performance report generated by the tool for a
dot-product of two packed float vectors of four elements. The analysis is
conducted for target x86, cpu btver2. The following result can be produced via
the following command using the example located at
test/tools/llvm-mca/X86/BtVer2/dot-product.s:

According to this report, the dot-product kernel has been executed 300 times,
for a total of 900 dynamically executed instructions.

The report is structured in three main sections. The first section collects a
few performance numbers; the goal of this section is to give a very quick
overview of the performance throughput. In this example, the two important
performance indicators are IPC and Block RThroughput (Block Reciprocal
Throughput).

IPC is computed dividing the total number of simulated instructions by the total
number of cycles. A delta between Dispatch Width and IPC is an indicator of a
performance issue. In the absence of loop-carried data dependencies, the
observed IPC tends to a theoretical maximum which can be computed by dividing
the number of instructions of a single iteration by the Block RThroughput.

IPC is bounded from above by the dispatch width. That is because the dispatch
width limits the maximum size of a dispatch group. IPC is also limited by the
amount of hardware parallelism. The availability of hardware resources affects
the resource pressure distribution, and it limits the number of instructions
that can be executed in parallel every cycle. A delta between Dispatch
Width and the theoretical maximum IPC is an indicator of a performance
bottleneck caused by the lack of hardware resources. In general, the lower the
Block RThroughput, the better.

In this example, Instructionsperiteration/BlockRThroughput is 1.50. Since
there are no loop-carried dependencies, the observed IPC is expected to approach
1.50 when the number of iterations tends to infinity. The delta between the
Dispatch Width (2.00), and the theoretical maximum IPC (1.50) is an indicator of
a performance bottleneck caused by the lack of hardware resources, and the
Resource pressure view can help to identify the problematic resource usage.

The second section of the report shows the latency and reciprocal
throughput of every instruction in the sequence. That section also reports
extra information related to the number of micro opcodes, and opcode properties
(i.e., ‘MayLoad’, ‘MayStore’, and ‘HasSideEffects’).

The third section is the Resource pressure view. This view reports
the average number of resource cycles consumed every iteration by instructions
for every processor resource unit available on the target. Information is
structured in two tables. The first table reports the number of resource cycles
spent on average every iteration. The second table correlates the resource
cycles to the machine instruction in the sequence. For example, every iteration
of the instruction vmulps always executes on resource unit [6]
(JFPU1 - floating point pipeline #1), consuming an average of 1 resource cycle
per iteration. Note that on AMD Jaguar, vector floating-point multiply can
only be issued to pipeline JFPU1, while horizontal floating-point additions can
only be issued to pipeline JFPU0.

The resource pressure view helps with identifying bottlenecks caused by high
usage of specific hardware resources. Situations with resource pressure mainly
concentrated on a few resources should, in general, be avoided. Ideally,
pressure should be uniformly distributed between multiple resources.

The timeline view produces a detailed report of each instruction’s state
transitions through an instruction pipeline. This view is enabled by the
command line option -timeline. As instructions transition through the
various stages of the pipeline, their states are depicted in the view report.
These states are represented by the following characters:

D : Instruction dispatched.

e : Instruction executing.

E : Instruction executed.

R : Instruction retired.

= : Instruction already dispatched, waiting to be executed.

- : Instruction executed, waiting to be retired.

Below is the timeline view for a subset of the dot-product example located in
test/tools/llvm-mca/X86/BtVer2/dot-product.s and processed by
llvm-mca using the following command:

The timeline view is interesting because it shows instruction state changes
during execution. It also gives an idea of how the tool processes instructions
executed on the target, and how their timing information might be calculated.

The timeline view is structured in two tables. The first table shows
instructions changing state over time (measured in cycles); the second table
(named Average Wait times) reports useful timing statistics, which should
help diagnose performance bottlenecks caused by long data dependencies and
sub-optimal usage of hardware resources.

An instruction in the timeline view is identified by a pair of indices, where
the first index identifies an iteration, and the second index is the
instruction index (i.e., where it appears in the code sequence). Since this
example was generated using 3 iterations: -iterations=3, the iteration
indices range from 0-2 inclusively.

Excluding the first and last column, the remaining columns are in cycles.
Cycles are numbered sequentially starting from 0.

From the example output above, we know the following:

Instruction [1,0] was dispatched at cycle 1.

Instruction [1,0] started executing at cycle 2.

Instruction [1,0] reached the write back stage at cycle 4.

Instruction [1,0] was retired at cycle 10.

Instruction [1,0] (i.e., vmulps from iteration #1) does not have to wait in the
scheduler’s queue for the operands to become available. By the time vmulps is
dispatched, operands are already available, and pipeline JFPU1 is ready to
serve another instruction. So the instruction can be immediately issued on the
JFPU1 pipeline. That is demonstrated by the fact that the instruction only
spent 1cy in the scheduler’s queue.

There is a gap of 5 cycles between the write-back stage and the retire event.
That is because instructions must retire in program order, so [1,0] has to wait
for [0,2] to be retired first (i.e., it has to wait until cycle 10).

In the example, all instructions are in a RAW (Read After Write) dependency
chain. Register %xmm2 written by vmulps is immediately used by the first
vhaddps, and register %xmm3 written by the first vhaddps is used by the second
vhaddps. Long data dependencies negatively impact the ILP (Instruction Level
Parallelism).

In the dot-product example, there are anti-dependencies introduced by
instructions from different iterations. However, those dependencies can be
removed at register renaming stage (at the cost of allocating register aliases,
and therefore consuming physical registers).

Table Average Wait times helps diagnose performance issues that are caused by
the presence of long latency instructions and potentially long data dependencies
which may limit the ILP. Note that llvm-mca, by default, assumes at
least 1cy between the dispatch event and the issue event.

When the performance is limited by data dependencies and/or long latency
instructions, the number of cycles spent while in the ready state is expected
to be very small when compared with the total number of cycles spent in the
scheduler’s queue. The difference between the two counters is a good indicator
of how large of an impact data dependencies had on the execution of the
instructions. When performance is mostly limited by the lack of hardware
resources, the delta between the two counters is small. However, the number of
cycles spent in the queue tends to be larger (i.e., more than 1-3cy),
especially when compared to other low latency instructions.

If we look at the Dynamic Dispatch Stall Cycles table, we see the counter for
SCHEDQ reports 272 cycles. This counter is incremented every time the dispatch
logic is unable to dispatch a group of two instructions because the scheduler’s
queue is full.

Looking at the Dispatch Logic table, we see that the pipeline was only able
to dispatch two instructions 51.5% of the time. The dispatch group was limited
to one instruction 44.6% of the cycles, which corresponds to 272 cycles. The
dispatch statistics are displayed by either using the command option
-all-stats or -dispatch-stats.

The next table, Schedulers, presents a histogram displaying a count,
representing the number of instructions issued on some number of cycles. In
this case, of the 610 simulated cycles, single
instructions were issued 306 times (50.2%) and there were 7 cycles where
no instructions were issued.

The dot-product is a kernel of three floating point instructions (a vector
multiply followed by two horizontal adds). That explains why only the floating
point scheduler appears to be used.

A full scheduler queue is either caused by data dependency chains or by a
sub-optimal usage of hardware resources. Sometimes, resource pressure can be
mitigated by rewriting the kernel using different instructions that consume
different scheduler resources. Schedulers with a small queue are less resilient
to bottlenecks caused by the presence of long data dependencies.
The scheduler statistics are displayed by
using the command option -all-stats or -scheduler-stats.

The next table, Retire Control Unit, presents a histogram displaying a count,
representing the number of instructions retired on some number of cycles. In
this case, of the 610 simulated cycles, two instructions were retired during
the same cycle 399 times (65.4%) and there were 109 cycles where no
instructions were retired. The retire statistics are displayed by using the
command option -all-stats or -retire-stats.

The last table presented is Register File statistics. Each physical register
file (PRF) used by the pipeline is presented in this table. In the case of AMD
Jaguar, there are two register files, one for floating-point registers
(JFpuPRF) and one for integer registers (JIntegerPRF). The table shows that of
the 900 instructions processed, there were 900 mappings created. Since this
dot-product example utilized only floating point registers, the JFPuPRF was
responsible for creating the 900 mappings. However, we see that the pipeline
only used a maximum of 35 of 72 available register slots at any given time. We
can conclude that the floating point PRF was the only register file used for
the example, and that it was never resource constrained. The register file
statistics are displayed by using the command option -all-stats or
-register-file-stats.

In this example, we can conclude that the IPC is mostly limited by data
dependencies, and not by resource pressure.

The default pipeline only models the out-of-order portion of a processor.
Therefore, the instruction fetch and decode stages are not modeled. Performance
bottlenecks in the frontend are not diagnosed. MCA assumes that instructions
have all been decoded and placed into a queue. Also, MCA does not model branch
prediction.

During the dispatch stage, instructions are picked in program order from a
queue of already decoded instructions, and dispatched in groups to the
simulated hardware schedulers.

The size of a dispatch group depends on the availability of the simulated
hardware resources. The processor dispatch width defaults to the value
of the IssueWidth in LLVM’s scheduling model.

An instruction can be dispatched if:

The size of the dispatch group is smaller than processor’s dispatch width.

There are enough entries in the reorder buffer.

There are enough physical registers to do register renaming.

The schedulers are not full.

Scheduling models can optionally specify which register files are available on
the processor. MCA uses that information to initialize register file
descriptors. Users can limit the number of physical registers that are
globally available for register renaming by using the command option
-register-file-size. A value of zero for this option means unbounded.
By knowing how many registers are available for renaming, MCA can predict
dispatch stalls caused by the lack of registers.

The number of reorder buffer entries consumed by an instruction depends on the
number of micro-opcodes specified by the target scheduling model. MCA’s
reorder buffer’s purpose is to track the progress of instructions that are
“in-flight,” and to retire instructions in program order. The number of
entries in the reorder buffer defaults to the MicroOpBufferSize provided by
the target scheduling model.

Instructions that are dispatched to the schedulers consume scheduler buffer
entries. llvm-mca queries the scheduling model to determine the set
of buffered resources consumed by an instruction. Buffered resources are
treated like scheduler resources.

Each processor scheduler implements a buffer of instructions. An instruction
has to wait in the scheduler’s buffer until input register operands become
available. Only at that point, does the instruction becomes eligible for
execution and may be issued (potentially out-of-order) for execution.
Instruction latencies are computed by llvm-mca with the help of the
scheduling model.

llvm-mca’s scheduler is designed to simulate multiple processor
schedulers. The scheduler is responsible for tracking data dependencies, and
dynamically selecting which processor resources are consumed by instructions.
It delegates the management of processor resource units and resource groups to a
resource manager. The resource manager is responsible for selecting resource
units that are consumed by instructions. For example, if an instruction
consumes 1cy of a resource group, the resource manager selects one of the
available units from the group; by default, the resource manager uses a
round-robin selector to guarantee that resource usage is uniformly distributed
between all units of a group.

llvm-mca’s scheduler implements three instruction queues:

WaitQueue: a queue of instructions whose operands are not ready.

ReadyQueue: a queue of instructions ready to execute.

IssuedQueue: a queue of instructions executing.

Depending on the operand availability, instructions that are dispatched to the
scheduler are either placed into the WaitQueue or into the ReadyQueue.

Every cycle, the scheduler checks if instructions can be moved from the
WaitQueue to the ReadyQueue, and if instructions from the ReadyQueue can be
issued to the underlying pipelines. The algorithm prioritizes older instructions
over younger instructions.

Issued instructions are moved from the ReadyQueue to the IssuedQueue. There,
instructions wait until they reach the write-back stage. At that point, they
get removed from the queue and the retire control unit is notified.

When instructions are executed, the retire control unit flags the
instruction as “ready to retire.”

Instructions are retired in program order. The register file is notified of
the retirement so that it can free the physical registers that were allocated
for the instruction during the register renaming stage.

To simulate an out-of-order execution of memory operations, llvm-mca
utilizes a simulated load/store unit (LSUnit) to simulate the speculative
execution of loads and stores.

Each load (or store) consumes an entry in the load (or store) queue. Users can
specify flags -lqueue and -squeue to limit the number of entries in the
load and store queues respectively. The queues are unbounded by default.

The LSUnit implements a relaxed consistency model for memory loads and stores.
The rules are:

A younger load is allowed to pass an older load only if there are no
intervening stores or barriers between the two loads.

A younger load is allowed to pass an older store provided that the load does
not alias with the store.

A younger store is not allowed to pass an older store.

A younger store is not allowed to pass an older load.

By default, the LSUnit optimistically assumes that loads do not alias
(-noalias=true) store operations. Under this assumption, younger loads are
always allowed to pass older stores. Essentially, the LSUnit does not attempt
to run any alias analysis to predict when loads and stores do not alias with
each other.

Note that, in the case of write-combining memory, rule 3 could be relaxed to
allow reordering of non-aliasing store operations. That being said, at the
moment, there is no way to further relax the memory model (-noalias is the
only option). Essentially, there is no option to specify a different memory
type (e.g., write-back, write-combining, write-through; etc.) and consequently
to weaken, or strengthen, the memory model.

Other limitations are:

The LSUnit does not know when store-to-load forwarding may occur.

The LSUnit does not know anything about cache hierarchy and memory types.

The LSUnit does not know how to identify serializing operations and memory
fences.

The LSUnit does not attempt to predict if a load or store hits or misses the L1
cache. It only knows if an instruction “MayLoad” and/or “MayStore.” For
loads, the scheduling model provides an “optimistic” load-to-use latency (which
usually matches the load-to-use latency for when there is a hit in the L1D).

llvm-mca does not know about serializing operations or memory-barrier
like instructions. The LSUnit conservatively assumes that an instruction which
has both “MayLoad” and unmodeled side effects behaves like a “soft”
load-barrier. That means, it serializes loads without forcing a flush of the
load queue. Similarly, instructions that “MayStore” and have unmodeled side
effects are treated like store barriers. A full memory barrier is a “MayLoad”
and “MayStore” instruction with unmodeled side effects. This is inaccurate, but
it is the best that we can do at the moment with the current information
available in LLVM.

A load/store barrier consumes one entry of the load/store queue. A load/store
barrier enforces ordering of loads/stores. A younger load cannot pass a load
barrier. Also, a younger store cannot pass a store barrier. A younger load
has to wait for the memory/load barrier to execute. A load/store barrier is
“executed” when it becomes the oldest entry in the load/store queue(s). That
also means, by construction, all of the older loads/stores have been executed.